Convolutional neural network for multiple particle identification in the MicroBooNE liquid argon time projection chamber

Abstract

We present the multiple particle identification (MPID) network, a convolutional neural network for multiple object classification, developed by MicroBooNE. MPID provides the probabilities that an interaction includes an e−, γ, μ−, π±, and protons in a liquid argon time projection chamber single readout plane. The network extends the single particle identification network previously developed by MicroBooNE [Convolutional neural networks applied to neutrino events in a liquid argon time projection chamber, R. Acciarri et al. J. Instrum. 12, P03011 (2017)]. MPID takes as input an image either cropped around a reconstructed interaction vertex or containing only activity connected to a reconstructed vertex, therefore relieving the tool from inefficiencies in vertex finding and particle clustering. The network serves as an important component in MicroBooNE’s deep-learning-based νe search analysis. In this paper, we present the network’s design, training, and performance on simulation and data from the MicroBooNE detector.29 MoreReceived 23 October 2020Accepted 25 March 2021DOI:https://doi.org/10.1103/PhysRevD.103.092003Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article’s title, journal citation, and DOI. Funded by SCOAP3.Published by the American Physical SocietyPhysics Subject Headings (PhySH)Physical SystemsNeutrinosTechniquesArtificial neural networksMachine learningParticles & Fields